To move to 2D, parallel \(d_\parallel \) 
and perpendicular \(d_\perp \)  distances are added to the set of input variables.
However, I was not able to achieve any reasonable inference
results with this configuration. Inspired by recent successes in neural
rendering, I added a Fourier Feature
layer (FF) [1]}, [2]}.
The FF layer takes the three input variables and converts them to 192
Fourier Features. The FF layer itself does not contain any trainable parameters.
The number of MLP parameters have nevertheless increased
to about 10,000 due to the increased input space and deeper architecture.
